library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Use built in datasets

data()

Using gapminder dataset pakage

library(gapminder)
## Warning: package 'gapminder' was built under R version 4.2.1

import data from net

ca <- read.csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/ca.csv")
glimpse(ca)
## Rows: 789
## Columns: 7
## $ region    <chr> "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", …
## $ state     <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", …
## $ code      <chr> "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHI…
## $ park_name <chr> "Channel Islands National Park", "Channel Islands National P…
## $ type      <chr> "National Park", "National Park", "National Park", "National…
## $ visitors  <int> 1200, 1500, 1600, 300, 15700, 31000, 33100, 32000, 24400, 31…
## $ year      <int> 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, …

1st plot

ggplot(ca,aes(x=year,y= visitors,color=park_name))+
  geom_point(alpha=.5)+
  labs(x="Year", y="Visitors", title= "National Park Visitors")+
  theme(
    legend.title = element_blank()
    
  )

ggplot(ca,aes(x=year,y= visitors,colour=park_name))+
  geom_point(alpha=.5)+
  facet_wrap(~code, scales = "free")+
  theme_bw()

ggplot(ca,aes(x=year,y= visitors,colour=state))+
  geom_point(alpha=.5)+
  labs(x="Year", y="Visitors", title= "Southeast States National Park Visitation")

  theme_light()
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Gap Minder data

library(tidyverse)
library(gapminder)
data <- gapminder 
glimpse(data)
## Rows: 1,704
## Columns: 6
## $ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …
ggplot(data , aes(x= log(gdpPercap), col= year, y= lifeExp , size=pop))+
  geom_jitter()+
  facet_wrap(~continent, scale="free")

Ex <- read.csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/se.csv")
glimpse(Ex)
## Rows: 453
## Columns: 7
## $ region    <chr> "SE", "SE", "SE", "SE", "SE", "SE", "SE", "SE", "SE", "SE", …
## $ state     <chr> "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", …
## $ code      <chr> "BISC", "BISC", "BISC", "BISC", "BISC", "BISC", "BISC", "BIS…
## $ park_name <chr> "Biscayne National Park", "Biscayne National Park", "Biscayn…
## $ type      <chr> "National Park", "National Park", "National Park", "National…
## $ visitors  <int> 78147, 243100, 409700, 351500, 339100, 205900, 177416, 20293…
## $ year      <int> 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, …
ggplot(Ex, aes(x=park_name, y=visitors, col= park_name))+
  geom_boxplot()+
  geom_jitter(alpha=0.3)+
  coord_flip()+
  theme_bw()

### interactive charts

library(plotly)
## Warning: package 'plotly' was built under R version 4.2.1
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
Analysis <- ggplot(Ex, aes(x=park_name, y=visitors, col= park_name))+
  geom_boxplot()+
  geom_jitter(alpha=0.3)+
  coord_flip()+
  theme_bw()

ggplotly()